Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates
the burden of annotation, but meanwhile suffers from the label noise. Recent
works attempt to adopt the teacher-student framework to gradually refine the
training labels and improve the overall robustness. However, we argue that
these teacher-student methods achieve limited performance because poor network
calibration produces incorrectly pseudo-labeled samples, leading to error
propagation. Therefore, we attempt to mitigate this issue by proposing: (1)
Uncertainty-aware Teacher Learning that leverages the prediction uncertainty to
guide the selection of pseudo-labels, avoiding the number of incorrect
pseudo-labels in the self-training stage. (2) Student-student Collaborative
Learning that allows the transfer of reliable labels between two student
networks instead of completely relying on all pseudo-labels from its teacher.
Meanwhile, this approach allows a full exploration of mislabeled samples rather
than simply filtering unreliable pseudo-labeled samples. Extensive experimental
results on five DS-NER datasets demonstrate that our method is superior to
state-of-the-art teacher-student methods.

提出了一种减轻标签噪声的 Distantly-Supervised Named Entity Recognition 方法，其中包括 Uncertainty-aware Teacher Learning 和 Student-student Collaborative Learning 两种技术。实验结果表明，该方法在五个 DS-NER 数据集上优于其他的 teacher-student 方法。

基于不确定性感知的远程监督命名实体识别中的教师学习和学生协同学习

Distantly-Supervised Named Entity Recognition with Uncertainty-aware  Teacher Learning and Student-student Collaborative Learning

Distantly-Supervised Named Entity Recognition effectively alleviates the
burden of time-consuming and expensive annotation in the supervised setting.
But the context-free matching process and the limited coverage of knowledge
bases introduce inaccurate and incomplete annotation noise respectively.
Previous studies either considered only incomplete annotation noise or
indiscriminately handle two types of noise with the same strategy. In this
paper, we argue that the different causes of two types of noise bring up the
requirement of different strategies in model architecture. Therefore, we
propose the SANTA to handle these two types of noise separately with (1)
Memory-smoothed Focal Loss and Entity-aware KNN to relieve the entity ambiguity
problem caused by inaccurate annotation, and (2) Boundary Mixup to alleviate
decision boundary shifting problem caused by incomplete annotation and a
noise-tolerant loss to improve the robustness. Benefiting from our separate
tailored strategies, we confirm in the experiment that the two types of noise
are well mitigated. SANTA also achieves a new state-of-the-art on five public
datasets.

本篇论文提出了一个新的名词实体识别模型 SANTA，其使用 Memory-smoothed Focal Loss 和 Entity-aware KNN 分别处理精度不准确和不完整噪音引起的实体模糊问题，使用 Boundary Mixup 和一种噪音容错的 loss 来缓解决策边界问题和提高模型的稳健性，实验结果表明 SANTA 可以有效缓解两种噪声类型，并在五个公共数据集上达到了新的最优表现。